Communicat.us

Major news outlets are leveraging social media sites for both getting news as it happens out as well as finding the next breaking story. There is both risk and reward to this phenomenon.

Let me preface the rest of this as being a shorter post that planned because I wrote the longer one and my editor crashed (ARGH).

The prompt for this post was the closing credit on the NBC Nightly News podcast. (For the curious, I get most of my news through various internet channels but I still like the capsulated format of the National Evening News. I just don’t like having to drop everything to watch the broadcast at a fixed time.) recently, NBC added a graphic to the end of the news to indicate they are on Twitter, Facebook, and support SMS.

News agencies have adopted the various social media as alternative channels to reach their audience (micro blogging breaking news, streaming video of extended features, etc.). More and more, they are also monitoring these channels for the next big story. The risk to "sourcing" from public social media is "verification". Ideally, all sources are vetted and corroborated, but it’s easy to cut corners in an attempt to scoop the story. It’s risky to get a story wrong. It’s far riskier to make business or security decisions.

There are two common methods of confirming data – authenticated sources (having security credentials and requiring a login to post content) or authoritative sources (building trust over time). Both mechanism have merit. However, controlling the credentials of all users, limits the scope of the user population. This is fine for a private solution but excludes the large data pools generated by public social media services. An alternative is to use analytics on the social media data to create a level of trust …

• how many different sources are reporting the same event
• what medium is used (micro blogging vs video vs SMS)
• what past traffic have the sources reported
• who "follows" the source
• what else is known about the sources
• etc

Monitoring the social media streams and analyzing the content goes far in established a level of credibility in the information. While there is risk in gathering information from unauthenticated sources, there is also great value assuming it is not trusted without verification.

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IBM, DARPA, and many of the current catch of reality TV shows are all interested in "stream computing".

DARPA had it’s LifeLog project to "captures, stores, and makes accessible the flow of one person’s experience". Whereas the TV series takes a 3-4 month slice of time for a group of people. The amount of data from these systems is massive and storing and post processing all of it is not really the point, especially when you consider that processing the stream would allow you to "focus" the capture process and make analysis and on-going objective. This is were IBM comes in.

IBM has been working on "stream computing" for the past 5 years. What first caught my attention was the "SmartBay" Environmental Monitoring System Installed in Galway Bay. The project is deploying advanced ocean sensors to collect and transmit real time information.

You can imagine using traditional analytics where data is captured and clustered into data cubes for processing. Once processed and analyzed, there would be a report and a series of recommendations. One of those would be of the form, "If we had had X data, we would have changed Y." this is where stream computing has the advantage.

“The beauty of SmartBay is that, for the first time in history, we can monitor a wide range of ocean conditions on a twenty-four hour basis … This opens up a wide range of possibilities for early warning systems for pollution, the study of fish and shellfish stocks, the prediction of harmful algal blooms, dangerously high waves and even the long term shifts in ocean conditions" – James Ryan

You can read more about Stream Computing here and the SmartBay project here.